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//
// Every input (prompt, thinking, answer) is fed as an embedding vector, so all modes share one code path and the
// discrete mode is an exact control:
// discrete next input = E[sampled token] (control)
// soft next input = sum_j p_j E[j] over the top --topk-soft candidates (Soft Thinking)
// gumbel next input = sum_j softmax((log p_j + g_j) / tau) E[j], g ~ Gumbel (stochastic soft thinking)
// moi next input = sum_j w_j E[j], w = (H p + (beta + 1 - H) onehot(sampled)) / (beta + 1),
// H = entropy / log(n_vocab) (Mixture of Inputs)
// mti discrete sampling, but where the entropy of the next-token distribution exceeds --tau-mti the logits are
// replaced by classifier-free guidance against a negative branch: the context continued by a short
// negative cue (--neg, " OUTPUT ERROR") in a scratch copy of the sequence; log p = (1 - w) log p_neg +
// w log p, w = --omega (Minimal Test-Time Intervention, ACL 2026). Applied while thinking and answering.
// Thinking ends when </think> is the chosen token (sampled for discrete/moi/mti, argmax of the weights for soft/gumbel),
// at --budget (forced), or for soft/gumbel when the entropy stays below --cold-tau for --cold-len steps ("cold stop").
// The answer is then sampled discretely in every mode.
// Sampling chain as in our llama-server runs: top_k 50 -> top_p 0.95 (temperature 1) -> temperature 0.6.
//
// --prompts: one line per prompt: id <TAB> token ids separated by spaces
// --out: id <TAB> think_steps <TAB> forced <TAB> cold <TAB> thinking ids <TAB> answer ids (appended)
// --selftest compares the token path with the embedding path, alone and batched.
#include "ggml-backend.h"
#include "llama.h"
#include <algorithm>
#include <chrono>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <random>
#include <set>
#include <sstream>
#include <string>
#include <vector>
static const llama_token THINK_END = 124902;
static const llama_token EOS_TOK = 124900;
enum Mode { DISCRETE, SOFT, GUMBEL, MOI, MTI };
struct Args {
std::string model, embd, prompts, out, mode = "discrete";
int budget = 6144, answer = 768, par = 64, top_k = 50, topk_soft = 15, cold_len = 256, ngl = 99, limit = 0;
float top_p = 0.95f, temp = 0.6f, tau = 0.5f, beta = 1.0f, cold_tau = 0.01f, tau_mti = 1.0f, omega = 1.5f;
int kvu = -1; // unified KV buffer: -1 = only for mti (cheap scratch copies), 0 / 1 = force
std::vector<llama_token> neg;
unsigned long long seed = 0;
bool selftest = false;
bool tokens = false; // discrete mode only: feed token ids instead of their embeddings (speed check)
};
struct Prompt {
std::string id;
std::vector<llama_token> toks;
};
struct Seq {
int p = -1; // prompt index, -1 = idle
int pos = 0;
int phase = 0; // 0 thinking, 1 answer, 2 finished
bool prefill = false;
int think_steps = 0, low_run = 0;
bool forced = false, cold = false;
std::vector<llama_token> think, answer;
std::vector<float> next; // next input embedding
llama_token next_tok = -1; // the same input as a token id, when it is one token
std::mt19937_64 rng;
int out = -1; // index of its logits in the batch
// MTI: guidance at this step, index of the negative branch's logits, conditional top-k ids and log-probabilities
bool cfg = false;
int out2 = -1;
std::vector<int> cid;
std::vector<float> clp;
};
static int n_embd = 0;
static int n_vocab = 0;
static std::vector<float> E; // n_vocab x n_embd
static const float * row_ptr(llama_token t) { return &E[(size_t) t * n_embd]; }
static void set_row(llama_token t, std::vector<float> & out) { out.assign(row_ptr(t), row_ptr(t) + n_embd); }
static void mix(const std::vector<int> & ids, const std::vector<float> & w, size_t k, std::vector<float> & out) {
out.assign(n_embd, 0.0f);
for (size_t j = 0; j < k; j++) {
if (w[j] == 0.0f) continue;
const float * e = row_ptr(ids[j]);
for (int d = 0; d < n_embd; d++) out[d] += w[j] * e[d];
}
}
// top_k -> top_p (temperature 1) -> temperature; ids sorted by probability, probabilities renormalised
static void filtered(const float * logits, const Args & a, std::vector<int> & ids, std::vector<float> & ps) {
std::vector<std::pair<float, int>> h;
h.reserve(a.top_k + 1);
auto gt = [](const std::pair<float, int> & x, const std::pair<float, int> & y) { return x.first > y.first; };
for (int i = 0; i < n_vocab; i++) {
const float v = logits[i];
if ((int) h.size() < a.top_k) {
h.push_back({ v, i });
std::push_heap(h.begin(), h.end(), gt);
} else if (v > h.front().first) {
std::pop_heap(h.begin(), h.end(), gt);
h.back() = { v, i };
std::push_heap(h.begin(), h.end(), gt);
}
}
std::sort(h.begin(), h.end(), gt);
const double mx = h[0].first;
std::vector<double> p1(h.size());
double s = 0;
for (size_t i = 0; i < h.size(); i++) s += (p1[i] = std::exp(h[i].first - mx));
size_t keep = 0;
double cum = 0;
for (size_t i = 0; i < h.size(); i++) {
keep = i + 1;
cum += p1[i] / s;
if (cum >= a.top_p) break;
}
ids.resize(keep);
ps.resize(keep);
double z = 0;
for (size_t i = 0; i < keep; i++) {
ids[i] = h[i].second;
z += (p1[i] = std::exp((h[i].first - mx) / a.temp));
}
for (size_t i = 0; i < keep; i++) ps[i] = (float) (p1[i] / z);
}
static void step(Seq & s, const float * logits, const Args & a, Mode mode) {
std::vector<int> ids;
std::vector<float> ps;
filtered(logits, a, ids, ps);
std::uniform_real_distribution<double> U(0.0, 1.0);
auto sample = [&]() {
const double u = U(s.rng);
double c = 0;
for (size_t j = 0; j < ps.size(); j++) {
c += ps[j];
if (u < c) return ids[j];
}
return ids.back();
};
if (s.phase == 1) { // answer: always discrete
const llama_token t = sample();
if (t == EOS_TOK || (int) s.answer.size() >= a.answer) {
s.phase = 2;
return;
}
s.answer.push_back(t);
set_row(t, s.next);
s.next_tok = t;
return;
}
llama_token chosen;
std::vector<float> w;
size_t k = 0;
if (mode == DISCRETE || mode == MTI) {
chosen = sample();
} else if (mode == MOI) {
chosen = sample();
double H = 0;
for (float p : ps)
if (p > 0) H -= p * std::log((double) p);
H /= std::log((double) n_vocab);
k = ps.size();
w.resize(k);
for (size_t j = 0; j < k; j++)
w[j] = (float) ((H * ps[j] + (ids[j] == chosen ? a.beta + 1.0 - H : 0.0)) / (a.beta + 1.0));
} else {
k = std::min((size_t) a.topk_soft, ps.size());
w.assign(ps.begin(), ps.begin() + k);
if (mode == GUMBEL) {
std::vector<double> z(k);
double mz = -1e300;
for (size_t j = 0; j < k; j++) {
const double g = -std::log(-std::log(std::max(U(s.rng), 1e-300)));
z[j] = (std::log(std::max((double) w[j], 1e-30)) + g) / a.tau;
mz = std::max(mz, z[j]);
}
double sum = 0;
for (size_t j = 0; j < k; j++) sum += (z[j] = std::exp(z[j] - mz));
for (size_t j = 0; j < k; j++) w[j] = (float) (z[j] / sum);
} else {
double sum = 0;
for (size_t j = 0; j < k; j++) sum += w[j];
for (size_t j = 0; j < k; j++) w[j] = (float) (w[j] / sum);
}
chosen = ids[std::max_element(w.begin(), w.end()) - w.begin()];
double H = 0;
for (float p : ps)
if (p > 0) H -= p * std::log((double) p);
s.low_run = H < a.cold_tau ? s.low_run + 1 : 0;
}
bool end = chosen == THINK_END;
if (!end && s.think_steps >= a.budget) end = s.forced = true;
if (!end && (mode == SOFT || mode == GUMBEL) && s.low_run >= a.cold_len) end = s.cold = true;
if (end) {
s.phase = 1;
set_row(THINK_END, s.next);
s.next_tok = THINK_END;
return;
}
s.think_steps++;
s.think.push_back(chosen);
if (mode == DISCRETE || mode == MTI) set_row(chosen, s.next), s.next_tok = chosen;
else mix(ids, w, k, s.next), s.next_tok = -1;
}
// entropy (nats) of softmax(logits) over the whole vocabulary
static double entropy(const float * l) {
double m = l[0];
for (int i = 1; i < n_vocab; i++) m = std::max(m, (double) l[i]);
double z = 0, s = 0;
for (int i = 0; i < n_vocab; i++) {
const double e = std::exp(l[i] - m);
z += e;
s += e * (l[i] - m);
}
return std::log(z) - s / z;
}
static double logsumexp(const float * l) {
double m = l[0];
for (int i = 1; i < n_vocab; i++) m = std::max(m, (double) l[i]);
double z = 0;
for (int i = 0; i < n_vocab; i++) z += std::exp(l[i] - m);
return m + std::log(z);
}
// conditional top-k candidates and their log-probabilities (guidance is applied among the plausible candidates only)
static void top_logprobs(const float * l, int k, std::vector<int> & ids, std::vector<float> & lp) {
std::vector<std::pair<float, int>> h;
auto gt = [](const std::pair<float, int> & x, const std::pair<float, int> & y) { return x.first > y.first; };
for (int i = 0; i < n_vocab; i++) {
if ((int) h.size() < k) {
h.push_back({ l[i], i });
std::push_heap(h.begin(), h.end(), gt);
} else if (l[i] > h.front().first) {
std::pop_heap(h.begin(), h.end(), gt);
h.back() = { l[i], i };
std::push_heap(h.begin(), h.end(), gt);
}
}
const double lse = logsumexp(l);
ids.resize(h.size());
lp.resize(h.size());
for (size_t j = 0; j < h.size(); j++) ids[j] = h[j].second, lp[j] = (float) (h[j].first - lse);
}
static unsigned long long fnv(const std::string & s) {
unsigned long long h = 1469598103934665603ULL;
for (unsigned char c : s) h = (h ^ c) * 1099511628211ULL;
return h;
}
static void add_embd(llama_batch & b, const float * e, llama_pos pos, llama_seq_id sq, bool out) {
const int i = b.n_tokens++;
std::memcpy(b.embd + (size_t) i * n_embd, e, sizeof(float) * n_embd);
b.pos[i] = pos;
b.n_seq_id[i] = 1;
b.seq_id[i][0] = sq;
b.logits[i] = out;
}
static Args parse(int argc, char ** argv) {
Args a;
for (int i = 1; i < argc; i++) {
std::string k = argv[i];
auto v = [&]() { return std::string(argv[++i]); };
if (k == "--model") a.model = v();
else if (k == "--embd") a.embd = v();
else if (k == "--prompts") a.prompts = v();
else if (k == "--out") a.out = v();
else if (k == "--mode") a.mode = v();
else if (k == "--budget") a.budget = std::stoi(v());
else if (k == "--answer") a.answer = std::stoi(v());
else if (k == "--par") a.par = std::stoi(v());
else if (k == "--topk-soft") a.topk_soft = std::stoi(v());
else if (k == "--cold-len") a.cold_len = std::stoi(v());
else if (k == "--cold-tau") a.cold_tau = std::stof(v());
else if (k == "--tau") a.tau = std::stof(v());
else if (k == "--beta") a.beta = std::stof(v());
else if (k == "--tau-mti") a.tau_mti = std::stof(v());
else if (k == "--omega") a.omega = std::stof(v());
else if (k == "--kvu") a.kvu = std::stoi(v());
else if (k == "--neg") {
std::istringstream ss(v());
int t;
while (ss >> t) a.neg.push_back(t);
}
else if (k == "--seed") a.seed = std::stoull(v());
else if (k == "--ngl") a.ngl = std::stoi(v());
else if (k == "--limit") a.limit = std::stoi(v());
else if (k == "--selftest") a.selftest = true;
else if (k == "--tokens") a.tokens = true;
else {
fprintf(stderr, "unknown argument %s\n", k.c_str());
exit(1);
}
}
return a;
}
static std::vector<Prompt> read_prompts(const std::string & path, int limit, const std::set<std::string> & skip) {
std::vector<Prompt> P;
std::ifstream f(path);
std::string line;
while (std::getline(f, line)) {
const size_t tab = line.find('\t');
if (tab == std::string::npos) continue;
Prompt p;
p.id = line.substr(0, tab);
std::istringstream ss(line.substr(tab + 1));
int t;
while (ss >> t) p.toks.push_back(t);
if (limit && (int) P.size() + (int) skip.size() >= limit) break;
if (!skip.count(p.id)) P.push_back(p);
}
return P;
}
static std::vector<float> logits_copy(llama_context * ctx, int i) {
const float * l = llama_get_logits_ith(ctx, i);
return std::vector<float>(l, l + n_vocab);
}
// KL(p_x || p_y) at temperature 1 (the logit noise that matters for sampling)
static float maxdiff(const std::vector<float> & x, const std::vector<float> & y) {
const double mx = *std::max_element(x.begin(), x.end()), my = *std::max_element(y.begin(), y.end());
double zx = 0, zy = 0;
for (size_t i = 0; i < x.size(); i++) zx += std::exp(x[i] - mx), zy += std::exp(y[i] - my);
double kl = 0;
for (size_t i = 0; i < x.size(); i++) {
const double lpx = x[i] - mx - std::log(zx), lpy = y[i] - my - std::log(zy);
kl += std::exp(lpx) * (lpx - lpy);
}
return (float) kl;
}
static int selftest(llama_context * ctx, const std::vector<Prompt> & P) {
llama_memory_t mem = llama_get_memory(ctx);
const auto & t0 = P[0].toks;
const auto & t1 = P[1].toks;
// A: token path, sequence 0 alone
llama_batch bt = llama_batch_init((int) t0.size() + 8, 0, 1);
bt.n_tokens = 0;
for (size_t j = 0; j < t0.size(); j++) {
const int i = bt.n_tokens++;
bt.token[i] = t0[j], bt.pos[i] = (llama_pos) j, bt.n_seq_id[i] = 1, bt.seq_id[i][0] = 0;
bt.logits[i] = j + 1 == t0.size();
}
if (llama_decode(ctx, bt)) return fprintf(stderr, "decode A failed\n"), 1;
auto A = logits_copy(ctx, bt.n_tokens - 1);
// greedy continuation, token path
std::vector<std::vector<float>> contA;
std::vector<llama_token> cont;
llama_token g = (llama_token) (std::max_element(A.begin(), A.end()) - A.begin());
for (int s = 0; s < 6; s++) {
cont.push_back(g);
bt.n_tokens = 1;
bt.token[0] = g, bt.pos[0] = (llama_pos) (t0.size() + s), bt.n_seq_id[0] = 1, bt.seq_id[0][0] = 0, bt.logits[0] = 1;
if (llama_decode(ctx, bt)) return fprintf(stderr, "decode A+ failed\n"), 1;
contA.push_back(logits_copy(ctx, 0));
g = (llama_token) (std::max_element(contA.back().begin(), contA.back().end()) - contA.back().begin());
}
llama_memory_clear(mem, true);
// B: embedding path, sequence 0 alone
llama_batch be = llama_batch_init((int) (t0.size() + t1.size()) + 8, n_embd, 1);
be.n_tokens = 0;
for (size_t j = 0; j < t0.size(); j++) add_embd(be, row_ptr(t0[j]), (llama_pos) j, 0, j + 1 == t0.size());
if (llama_decode(ctx, be)) return fprintf(stderr, "decode B failed\n"), 1;
auto B = logits_copy(ctx, be.n_tokens - 1);
llama_memory_clear(mem, true);
// C: embedding path, sequences 0 and 1 in one batch, then 6 steps together
be.n_tokens = 0;
for (size_t j = 0; j < t1.size(); j++) add_embd(be, row_ptr(t1[j]), (llama_pos) j, 1, j + 1 == t1.size());
for (size_t j = 0; j < t0.size(); j++) add_embd(be, row_ptr(t0[j]), (llama_pos) j, 0, j + 1 == t0.size());
if (llama_decode(ctx, be)) return fprintf(stderr, "decode C failed\n"), 1;
auto C = logits_copy(ctx, be.n_tokens - 1);
float dc = 0;
for (int s = 0; s < 6; s++) {
be.n_tokens = 0;
add_embd(be, row_ptr(cont[s]), (llama_pos) (t1.size() + s), 1, true);
add_embd(be, row_ptr(cont[s]), (llama_pos) (t0.size() + s), 0, true);
if (llama_decode(ctx, be)) return fprintf(stderr, "decode C+ failed\n"), 1;
dc = std::max(dc, maxdiff(contA[s], logits_copy(ctx, 1)));
}
float scale = 0;
for (float x : A) scale = std::max(scale, std::fabs(x));
printf("KL divergence (nats), logit scale %.2f | prefill: token vs embedding %.4g, alone vs batched %.4g | 6 decode "
"steps, token alone vs embedding batched (max) %.4g\n",
scale, maxdiff(A, B), maxdiff(A, C), dc);
llama_memory_clear(mem, true);
return 0;
}
int main(int argc, char ** argv) {
Args a = parse(argc, argv);
const Mode mode = a.mode == "soft" ? SOFT : a.mode == "gumbel" ? GUMBEL : a.mode == "moi" ? MOI : a.mode == "mti" ? MTI
: DISCRETE;
if (a.mode != "soft" && a.mode != "gumbel" && a.mode != "moi" && a.mode != "mti" && a.mode != "discrete") {
fprintf(stderr, "unknown mode\n");
return 1;
}
if (mode == MTI && a.neg.empty()) {
fprintf(stderr, "mti needs --neg token ids\n");
return 1;
}
std::set<std::string> done;
if (!a.selftest && !a.out.empty()) {
std::ifstream f(a.out);
std::string line;
while (std::getline(f, line)) done.insert(line.substr(0, line.find('\t')));
}
std::vector<Prompt> P = read_prompts(a.prompts, a.limit, done);
std::sort(P.begin(), P.end(), [](const Prompt & x, const Prompt & y) { return x.toks.size() > y.toks.size(); });
if (P.empty()) {
printf("nothing to do\n");
return 0;
}
size_t maxlen = 0;
for (auto & p : P) maxlen = std::max(maxlen, p.toks.size());
llama_backend_init();
ggml_backend_load_all();
auto mp = llama_model_default_params();
mp.n_gpu_layers = a.ngl;
llama_model * model = llama_model_load_from_file(a.model.c_str(), mp);
if (!model) return fprintf(stderr, "model load failed\n"), 1;
n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
n_embd = llama_model_n_embd(model);
{
std::ifstream f(a.embd, std::ios::binary | std::ios::ate);
const size_t bytes = (size_t) f.tellg();
if (bytes < (size_t) n_vocab * n_embd * sizeof(float))
return fprintf(stderr, "embedding file too small: %zu bytes for %d x %d\n", bytes, n_vocab, n_embd), 1;
f.seekg(0);
E.resize((size_t) n_vocab * n_embd);
f.read((char *) E.data(), E.size() * sizeof(float));
}
const int n_batch = 2048 + a.par;
auto cp = llama_context_default_params();
cp.n_seq_max = mode == MTI ? 2 * a.par : a.par; // MTI: scratch copy par + i of sequence i
cp.n_ctx = (uint32_t) (a.par * (a.budget + a.answer + maxlen + 16 + a.neg.size()));
cp.n_batch = std::max(n_batch, (int) maxlen + a.par);
cp.n_ubatch = 512;
cp.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
// a unified buffer makes every sequence attend over all cells (masked): slow for many independent sequences
cp.kv_unified = a.kvu < 0 ? mode == MTI : a.kvu == 1;
cp.n_threads = 8;
cp.n_threads_batch = 8;
cp.no_perf = true;
llama_context * ctx = llama_init_from_model(model, cp);
if (!ctx) return fprintf(stderr, "context failed\n"), 1;
printf("model n_vocab %d n_embd %d | %zu prompts, mode %s, par %d, n_ctx %u\n", n_vocab, n_embd, P.size(),
a.mode.c_str(), a.par, cp.n_ctx);
fflush(stdout);
if (a.selftest) return selftest(ctx, P);
llama_memory_t mem = llama_get_memory(ctx);
llama_batch b = llama_batch_init(cp.n_batch, n_embd, 1);
llama_batch b2 = llama_batch_init(a.par * (int) a.neg.size() + 1, n_embd, 1);
llama_batch bt = llama_batch_init(cp.n_batch, 0, 1);
std::vector<Seq> S(a.par);
FILE * fo = fopen(a.out.c_str(), "ab");
size_t next_p = 0, n_done = 0;
long long n_fed = 0, n_steps = 0, n_cfg = 0, n_seqsteps = 0;
const auto t0 = std::chrono::steady_clock::now();
std::vector<const float *> L(a.par);
double t_dec = 0, t_smp = 0;
auto now = [] { return std::chrono::steady_clock::now(); };
auto ms = [](auto x, auto y) { return std::chrono::duration<double, std::milli>(y - x).count(); };
while (true) {
int room = 2048; // prompt tokens admitted per step
for (int i = 0; i < a.par && next_p < P.size(); i++) {
if (S[i].p >= 0) continue;
const int n = (int) P[next_p].toks.size();
if (n > room && room < 2048) break;
S[i] = Seq();
S[i].p = (int) next_p;
S[i].prefill = true;
S[i].rng.seed(a.seed * 1000003ULL + fnv(P[next_p].id));
room -= n;
next_p++;
}
const bool use_tok = a.tokens && mode == DISCRETE;
b.n_tokens = bt.n_tokens = 0;
auto add_tok = [&](llama_token t, llama_pos pos, llama_seq_id sq, bool out) {
const int k = bt.n_tokens++;
bt.token[k] = t, bt.pos[k] = pos, bt.n_seq_id[k] = 1, bt.seq_id[k][0] = sq, bt.logits[k] = out;
};
for (int i = 0; i < a.par; i++) {
Seq & s = S[i];
s.out = -1;
if (s.p < 0) continue;
if (s.prefill) {
const auto & t = P[s.p].toks;
for (size_t j = 0; j < t.size(); j++)
if (use_tok) add_tok(t[j], (llama_pos) j, i, j + 1 == t.size());
else add_embd(b, row_ptr(t[j]), (llama_pos) j, i, j + 1 == t.size());
s.pos = (int) t.size();
s.prefill = false;
} else if (use_tok) {
add_tok(s.next_tok, s.pos++, i, true);
} else {
add_embd(b, s.next.data(), s.pos++, i, true);
}
s.out = (use_tok ? bt.n_tokens : b.n_tokens) - 1;
}
if (use_tok) b.n_tokens = bt.n_tokens;
if (b.n_tokens == 0) break;
n_fed += b.n_tokens;
for (int i = 0; i < a.par; i++) n_seqsteps += S[i].out >= 0;
auto tA = now();
const int rc = llama_decode(ctx, use_tok ? bt : b);
if (rc != 0) return fprintf(stderr, "llama_decode failed: %d\n", rc), 1;
for (int i = 0; i < a.par; i++) L[i] = S[i].out >= 0 ? llama_get_logits_ith(ctx, S[i].out) : nullptr;
auto tB = now();
t_dec += ms(tA, tB);
if (mode == MTI) {
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < a.par; i++) {
S[i].cfg = L[i] && entropy(L[i]) > a.tau_mti;
if (S[i].cfg) top_logprobs(L[i], a.top_k, S[i].cid, S[i].clp);
else if (L[i]) step(S[i], L[i], a, mode);
}
b2.n_tokens = 0;
for (int i = 0; i < a.par; i++) {
if (!S[i].cfg) continue;
llama_memory_seq_cp(mem, i, a.par + i, -1, -1);
for (size_t j = 0; j < a.neg.size(); j++)
add_embd(b2, row_ptr(a.neg[j]), (llama_pos) (S[i].pos + j), a.par + i, j + 1 == a.neg.size());
S[i].out2 = b2.n_tokens - 1;
n_cfg++;
}
if (b2.n_tokens) {
const int rc2 = llama_decode(ctx, b2);
if (rc2 != 0) return fprintf(stderr, "llama_decode (negative branch) failed: %d\n", rc2), 1;
for (int i = 0; i < a.par; i++) L[i] = S[i].cfg ? llama_get_logits_ith(ctx, S[i].out2) : nullptr;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < a.par; i++) {
if (!S[i].cfg) continue;
const double lse = logsumexp(L[i]);
std::vector<float> g(n_vocab, -1e30f);
for (size_t j = 0; j < S[i].cid.size(); j++) {
const int t = S[i].cid[j];
g[t] = (float) ((1.0 - a.omega) * (L[i][t] - lse) + a.omega * S[i].clp[j]);
}
step(S[i], g.data(), a, mode);
}
for (int i = 0; i < a.par; i++)
if (S[i].cfg) llama_memory_seq_rm(mem, a.par + i, -1, -1);
}
} else {
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < a.par; i++)
if (L[i]) step(S[i], L[i], a, mode);
}
t_smp += ms(tB, now());
for (int i = 0; i < a.par; i++) {
Seq & s = S[i];
if (s.p < 0 || s.phase != 2) continue;
fprintf(fo, "%s\t%d\t%d\t%d\t", P[s.p].id.c_str(), s.think_steps, (int) s.forced, (int) s.cold);
for (size_t j = 0; j < s.think.size(); j++) fprintf(fo, j ? " %d" : "%d", s.think[j]);
fprintf(fo, "\t");
for (size_t j = 0; j < s.answer.size(); j++) fprintf(fo, j ? " %d" : "%d", s.answer[j]);
fprintf(fo, "\n");
fflush(fo);
llama_memory_seq_rm(mem, i, -1, -1);
s.p = -1;
n_done++;
}
if (++n_steps % 200 == 0) {
const double el = std::chrono::duration<double>(std::chrono::steady_clock::now() - t0).count();
fprintf(stderr, "step %lld: %zu/%zu done %.0f tok/s %.1f min guidance at %.1f%% of steps (decode %.1f ms, "
"sampling %.1f ms per step)\n", n_steps, n_done, P.size(), n_fed / el, el / 60,
100.0 * n_cfg / std::max(1LL, n_seqsteps), t_dec / n_steps, t_smp / n_steps);
}
}
const double el = std::chrono::duration<double>(std::chrono::steady_clock::now() - t0).count();
printf("%zu/%zu done %.0f tok/s %.1f min guidance at %.1f%% of steps\n", n_done, P.size(), n_fed / el, el / 60,
100.0 * n_cfg / std::max(1LL, n_seqsteps));
fclose(fo);
llama_batch_free(b);
llama_batch_free(b2);
llama_free(ctx);
llama_model_free(model);
return 0;
}
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